Is it possible to use a for loop to change which linear regression to use for optimization purposes?

I'm trying to optimize my code where I want to discover which linear regression model is optimal to use, looking at R^2. So far this is what I've come up with (where M1 and M2 are parts of two tables):

[linear1, gof]=fit(M1,M2,'poly1');
linear1_R2=gof.rsquare
% 
[linear2, gof]=fit(M1,M2,'poly2');
linear2_R2=gof.rsquare
% 
[linear3, gof]=fit(M1,M2,'poly3');
linear3_R2=gof.rsquare
...

I want to do this until poly9 and it feels like it can be optimized. I was thinking about doing a for loop where the program (1) will run all the fit functions and (2) display which one gives the highest R^2 and what that value is, but I'm not sure how to proceed.

Hope someone can help, thanks in advance!

Answer 1

[Rishabh Gupta]

Hi Anna,

The below code snippet can accomplish the task. It fits all the models specified it 'fit_func' array and records the R^2 value in a vector. Finally, the model with the highest R^2 value can be easily extracted.

fit_func={'poly1','poly2','poly3','poly4','poly5','poly6','poly7','poly8','poly9'};
rsquare_values=zeros(length(fit_func),1);
for i=1:length(fit_func)
    [linear1, gof]=fit(M1,M2,fit_func{i});
    rsquare_values(i)=gof.rsquare;
end
[best_rsquare,index] = max(rsquare_values);
best_model = fit_func(index);

best_rsquare - highest R^2 value corresponding to the best model.

best_model - contains the model with the highest R^2 value.